# Copyright 2023-2024 SGLang Team
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""
The entry point of inference server. (SRT = SGLang Runtime)

This file implements HTTP APIs for the inference engine via fastapi.
"""

import asyncio
import dataclasses
import logging
import multiprocessing
import os
import tempfile
import threading
import time
from http import HTTPStatus
from typing import Any, AsyncIterator, Callable, Dict, List, Optional, Union

from sglang.srt.tracing.trace import process_tracing_init, trace_set_thread_info

# Fix a bug of Python threading
setattr(threading, "_register_atexit", lambda *args, **kwargs: None)

from contextlib import asynccontextmanager
from typing import AsyncGenerator

import numpy as np
import orjson
import requests
import uvicorn
import uvloop
from fastapi import Depends, FastAPI, HTTPException, Request, UploadFile
from fastapi.exceptions import RequestValidationError
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import ORJSONResponse, Response, StreamingResponse

from sglang.srt.disaggregation.utils import FAKE_BOOTSTRAP_HOST, DisaggregationMode
from sglang.srt.entrypoints.engine import _launch_subprocesses
from sglang.srt.entrypoints.openai.protocol import (
    ChatCompletionRequest,
    ClassifyRequest,
    CompletionRequest,
    DetokenizeRequest,
    EmbeddingRequest,
    ErrorResponse,
    ModelCard,
    ModelList,
    ResponsesRequest,
    ScoringRequest,
    TokenizeRequest,
    V1RerankReqInput,
)
from sglang.srt.entrypoints.openai.serving_chat import OpenAIServingChat
from sglang.srt.entrypoints.openai.serving_classify import OpenAIServingClassify
from sglang.srt.entrypoints.openai.serving_completions import OpenAIServingCompletion
from sglang.srt.entrypoints.openai.serving_embedding import OpenAIServingEmbedding
from sglang.srt.entrypoints.openai.serving_rerank import OpenAIServingRerank
from sglang.srt.entrypoints.openai.serving_score import OpenAIServingScore
from sglang.srt.entrypoints.openai.serving_tokenize import (
    OpenAIServingDetokenize,
    OpenAIServingTokenize,
)
from sglang.srt.entrypoints.warmup import execute_warmups
from sglang.srt.environ import envs
from sglang.srt.function_call.function_call_parser import FunctionCallParser
from sglang.srt.managers.io_struct import (
    AbortReq,
    CheckWeightsReqInput,
    CloseSessionReqInput,
    ConfigureLoggingReq,
    ContinueGenerationReqInput,
    DestroyWeightsUpdateGroupReqInput,
    EmbeddingReqInput,
    GenerateReqInput,
    GetWeightsByNameReqInput,
    InitWeightsSendGroupForRemoteInstanceReqInput,
    InitWeightsUpdateGroupReqInput,
    LoadLoRAAdapterReqInput,
    OpenSessionReqInput,
    ParseFunctionCallReq,
    PauseGenerationReqInput,
    ProfileReqInput,
    ReleaseMemoryOccupationReqInput,
    ResumeMemoryOccupationReqInput,
    SendWeightsToRemoteInstanceReqInput,
    SeparateReasoningReqInput,
    SetInternalStateReq,
    SlowDownReqInput,
    UnloadLoRAAdapterReqInput,
    UpdateWeightFromDiskReqInput,
    UpdateWeightsFromDistributedReqInput,
    UpdateWeightsFromIPCReqInput,
    UpdateWeightsFromTensorReqInput,
    UpdateWeightVersionReqInput,
    VertexGenerateReqInput,
)
from sglang.srt.managers.multi_tokenizer_mixin import (
    MultiTokenizerRouter,
    TokenizerWorker,
    get_main_process_id,
    monkey_patch_uvicorn_multiprocessing,
    read_from_shared_memory,
    write_data_for_multi_tokenizer,
)
from sglang.srt.managers.template_manager import TemplateManager
from sglang.srt.managers.tokenizer_manager import ServerStatus, TokenizerManager
from sglang.srt.metrics.func_timer import enable_func_timer
from sglang.srt.parser.reasoning_parser import ReasoningParser
from sglang.srt.server_args import PortArgs, ServerArgs
from sglang.srt.utils import (
    add_api_key_middleware,
    add_prometheus_middleware,
    delete_directory,
    get_bool_env_var,
    kill_process_tree,
    set_uvicorn_logging_configs,
)
from sglang.utils import get_exception_traceback
from sglang.version import __version__

logger = logging.getLogger(__name__)
asyncio.set_event_loop_policy(uvloop.EventLoopPolicy())

HEALTH_CHECK_TIMEOUT = int(os.getenv("SGLANG_HEALTH_CHECK_TIMEOUT", 20))
WAIT_WEIGHTS_READY_TIMEOUT = int(os.getenv("SGLANG_WAIT_WEIGHTS_READY_TIMEOUT", 120))


# Store global states
@dataclasses.dataclass
class _GlobalState:
    tokenizer_manager: Union[TokenizerManager, MultiTokenizerRouter, TokenizerWorker]
    template_manager: TemplateManager
    scheduler_info: Dict


_global_state: Optional[_GlobalState] = None


def set_global_state(global_state: _GlobalState):
    global _global_state
    _global_state = global_state


async def init_multi_tokenizer() -> ServerArgs:
    """Read args information from shm and init tokenizer manager for current process"""

    # Read configuration from shared memory
    main_pid = get_main_process_id()
    port_args, server_args, scheduler_info = read_from_shared_memory(
        f"multi_tokenizer_args_{main_pid}"
    )
    server_args: ServerArgs
    port_args: PortArgs

    # API key authentication is not supported in multi-tokenizer mode
    assert (
        server_args.api_key is None
    ), "API key is not supported in multi-tokenizer mode"

    # Create a new ipc name for the current process
    port_args.tokenizer_ipc_name = (
        f"ipc://{tempfile.NamedTemporaryFile(delete=False).name}"
    )
    logger.info(
        f"Start multi-tokenizer worker process {os.getpid()}, "
        f"ipc_name={port_args.tokenizer_ipc_name}"
    )

    # Launch multi-tokenizer manager process
    tokenizer_manager = TokenizerWorker(server_args, port_args)
    template_manager = TemplateManager()
    template_manager.initialize_templates(
        tokenizer_manager=tokenizer_manager,
        model_path=server_args.model_path,
        chat_template=server_args.chat_template,
        completion_template=server_args.completion_template,
    )

    tokenizer_manager.max_req_input_len = scheduler_info["max_req_input_len"]

    set_global_state(
        _GlobalState(
            tokenizer_manager=tokenizer_manager,
            template_manager=template_manager,
            scheduler_info=scheduler_info,
        )
    )

    return server_args


@asynccontextmanager
async def lifespan(fast_api_app: FastAPI):
    if getattr(fast_api_app, "is_single_tokenizer_mode", False):
        server_args = fast_api_app.server_args
        warmup_thread_args = fast_api_app.warmup_thread_args
        thread_label = "Tokenizer"
    else:
        # Initialize multi-tokenizer support for worker processes
        server_args = await init_multi_tokenizer()
        warmup_thread_args = (
            server_args,
            None,
            None,
        )
        thread_label = f"MultiTokenizer-{_global_state.tokenizer_manager.worker_id}"

    # Add prometheus middleware
    if server_args.enable_metrics:
        add_prometheus_middleware(app)
        enable_func_timer()

    # Init tracing
    if server_args.enable_trace:
        process_tracing_init(server_args.otlp_traces_endpoint, "sglang")
        if server_args.disaggregation_mode == "prefill":
            thread_label = "Prefill" + thread_label
        elif server_args.disaggregation_mode == "decode":
            thread_label = "Decode" + thread_label
        trace_set_thread_info(thread_label)

    # Initialize OpenAI serving handlers
    fast_api_app.state.openai_serving_completion = OpenAIServingCompletion(
        _global_state.tokenizer_manager, _global_state.template_manager
    )
    fast_api_app.state.openai_serving_chat = OpenAIServingChat(
        _global_state.tokenizer_manager, _global_state.template_manager
    )
    fast_api_app.state.openai_serving_embedding = OpenAIServingEmbedding(
        _global_state.tokenizer_manager, _global_state.template_manager
    )
    fast_api_app.state.openai_serving_classify = OpenAIServingClassify(
        _global_state.tokenizer_manager, _global_state.template_manager
    )
    fast_api_app.state.openai_serving_score = OpenAIServingScore(
        _global_state.tokenizer_manager
    )
    fast_api_app.state.openai_serving_rerank = OpenAIServingRerank(
        _global_state.tokenizer_manager
    )
    fast_api_app.state.openai_serving_tokenize = OpenAIServingTokenize(
        _global_state.tokenizer_manager
    )
    fast_api_app.state.openai_serving_detokenize = OpenAIServingDetokenize(
        _global_state.tokenizer_manager
    )

    # Launch tool server
    tool_server = None
    if server_args.tool_server == "demo":
        from sglang.srt.entrypoints.openai.tool_server import DemoToolServer

        tool_server = DemoToolServer()
    elif server_args.tool_server:
        from sglang.srt.entrypoints.openai.tool_server import MCPToolServer

        tool_server = MCPToolServer()
        await tool_server.add_tool_server(server_args.tool_server)

    try:
        from sglang.srt.entrypoints.openai.serving_responses import (
            OpenAIServingResponses,
        )

        fast_api_app.state.openai_serving_responses = OpenAIServingResponses(
            _global_state.tokenizer_manager,
            _global_state.template_manager,
            enable_prompt_tokens_details=True,
            enable_force_include_usage=True,
            tool_server=tool_server,
        )
    except Exception:
        traceback = get_exception_traceback()
        logger.warning(f"Can not initialize OpenAIServingResponses, error: {traceback}")

    # Execute custom warmups
    if server_args.warmups is not None:
        await execute_warmups(
            server_args.disaggregation_mode,
            server_args.warmups.split(","),
            _global_state.tokenizer_manager,
        )
        logger.info("Warmup ended")

    # Execute the general warmup
    warmup_thread = threading.Thread(
        target=_wait_and_warmup,
        args=warmup_thread_args,
    )
    warmup_thread.start()

    # Start the HTTP server
    try:
        yield
    finally:
        warmup_thread.join()


# Fast API
app = FastAPI(
    lifespan=lifespan,
    openapi_url=None if get_bool_env_var("DISABLE_OPENAPI_DOC") else "/openapi.json",
)
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)


@app.exception_handler(HTTPException)
async def validation_exception_handler(request: Request, exc: HTTPException):
    """Enrich HTTP exception with status code and other details.

    For /v1/responses, emit OpenAI-style nested error envelope:
    {"error": {"message": "...", "type": "...", "param": null, "code": <status>}}
    """
    # adjust fmt for responses api
    if request.url.path.startswith("/v1/responses"):
        nested_error = {
            "message": exc.detail,
            "type": HTTPStatus(exc.status_code).phrase,
            "param": None,
            "code": exc.status_code,
        }
        return ORJSONResponse(
            content={"error": nested_error}, status_code=exc.status_code
        )

    error = ErrorResponse(
        object="error",
        message=exc.detail,
        type=str(exc.status_code),
        code=exc.status_code,
    )
    return ORJSONResponse(content=error.model_dump(), status_code=exc.status_code)


# Custom exception handlers to change validation error status codes
@app.exception_handler(RequestValidationError)
async def validation_exception_handler(request: Request, exc: RequestValidationError):
    """Override FastAPI's default 422 validation error with 400.

    For /v1/responses, emit OpenAI-style nested error envelope; for other endpoints keep legacy format.
    """
    exc_str = str(exc)
    errors_str = str(exc.errors())

    if errors_str and errors_str != exc_str:
        message = f"{exc_str} {errors_str}"
    else:
        message = exc_str

    if request.url.path.startswith("/v1/responses"):
        # adapt specially, for v1/responses API only (notice the error key is different)
        nested_error = {
            "message": message,
            "type": HTTPStatus.BAD_REQUEST.phrase,
            "param": None,
            "code": HTTPStatus.BAD_REQUEST.value,
        }
        return ORJSONResponse(status_code=400, content={"error": nested_error})

    err = ErrorResponse(
        message=message,
        type=HTTPStatus.BAD_REQUEST.phrase,
        code=HTTPStatus.BAD_REQUEST.value,
    )

    return ORJSONResponse(
        status_code=400,
        content=err.model_dump(),
    )


async def validate_json_request(raw_request: Request):
    """Validate that the request content-type is application/json."""
    content_type = raw_request.headers.get("content-type", "").lower()
    media_type = content_type.split(";", maxsplit=1)[0]
    if media_type != "application/json":
        raise RequestValidationError(
            errors=[
                {
                    "loc": ["header", "content-type"],
                    "msg": "Unsupported Media Type: Only 'application/json' is allowed",
                    "type": "value_error",
                }
            ]
        )


##### Native API endpoints #####


@app.get("/health")
@app.get("/health_generate")
async def health_generate(request: Request) -> Response:
    """
    Check the health of the inference server by sending a special request to generate one token.

    If the server is running something, this request will be ignored, so it creates zero overhead.
    If the server is not running anything, this request will be run, so we know whether the server is healthy.
    """

    if _global_state.tokenizer_manager.gracefully_exit:
        logger.info("Health check request received during shutdown. Returning 503.")
        return Response(status_code=503)

    if _global_state.tokenizer_manager.server_status == ServerStatus.Starting:
        return Response(status_code=503)

    if (
        not envs.SGLANG_ENABLE_HEALTH_ENDPOINT_GENERATION.get()
        and request.url.path == "/health"
    ):
        return Response(status_code=200)

    sampling_params = {"max_new_tokens": 1, "temperature": 0.0}
    rid = f"HEALTH_CHECK_{time.time()}"

    if _global_state.tokenizer_manager.is_image_gen:
        # Keep this branch for some internal use cases.
        raise NotImplementedError("Image generation is not supported yet.")
    elif _global_state.tokenizer_manager.is_generation:
        gri = GenerateReqInput(
            rid=rid,
            input_ids=[0],
            sampling_params=sampling_params,
            log_metrics=False,
        )
        if (
            _global_state.tokenizer_manager.server_args.disaggregation_mode
            != DisaggregationMode.NULL
        ):
            gri.bootstrap_host = FAKE_BOOTSTRAP_HOST
            gri.bootstrap_room = 0
    else:
        gri = EmbeddingReqInput(
            rid=rid, input_ids=[0], sampling_params=sampling_params, log_metrics=False
        )

    async def gen():
        async for _ in _global_state.tokenizer_manager.generate_request(gri, request):
            break

    task = asyncio.create_task(gen())

    # As long as we receive any response from the detokenizer/scheduler, we consider the server is healthy.
    tic = time.time()
    while time.time() < tic + HEALTH_CHECK_TIMEOUT:
        await asyncio.sleep(1)
        if _global_state.tokenizer_manager.last_receive_tstamp > tic:
            task.cancel()
            _global_state.tokenizer_manager.rid_to_state.pop(rid, None)
            _global_state.tokenizer_manager.server_status = ServerStatus.Up
            return Response(status_code=200)

    task.cancel()
    tic_time = time.strftime("%H:%M:%S", time.localtime(tic))
    last_receive_time = time.strftime(
        "%H:%M:%S", time.localtime(_global_state.tokenizer_manager.last_receive_tstamp)
    )
    logger.error(
        f"Health check failed. Server couldn't get a response from detokenizer for last "
        f"{HEALTH_CHECK_TIMEOUT} seconds. tic start time: {tic_time}. "
        f"last_heartbeat time: {last_receive_time}"
    )
    _global_state.tokenizer_manager.rid_to_state.pop(rid, None)
    _global_state.tokenizer_manager.server_status = ServerStatus.UnHealthy
    return Response(status_code=503)


@app.get("/get_model_info")
async def get_model_info():
    """Get the model information (deprecated - use /model_info instead)."""
    logger.warning(
        "Endpoint '/get_model_info' is deprecated and will be removed in a future version. "
        "Please use '/model_info' instead."
    )
    return await model_info()


@app.get("/model_info")
async def model_info():
    """Get the model information."""
    model_config = _global_state.tokenizer_manager.model_config
    result = {
        "model_path": _global_state.tokenizer_manager.model_path,
        "tokenizer_path": _global_state.tokenizer_manager.server_args.tokenizer_path,
        "is_generation": _global_state.tokenizer_manager.is_generation,
        "preferred_sampling_params": _global_state.tokenizer_manager.server_args.preferred_sampling_params,
        "weight_version": _global_state.tokenizer_manager.server_args.weight_version,
        "has_image_understanding": model_config.is_image_understandable_model,
        "has_audio_understanding": model_config.is_audio_understandable_model,
        "model_type": getattr(model_config.hf_config, "model_type", None),
        "architectures": getattr(model_config.hf_config, "architectures", None),
    }
    return result


@app.get("/get_weight_version")
async def get_weight_version():
    """Get the current weight version (deprecated - use /weight_version instead)."""
    logger.warning(
        "Endpoint '/get_weight_version' is deprecated and will be removed in a future version. "
        "Please use '/weight_version' instead."
    )
    return await weight_version()


@app.get("/weight_version")
async def weight_version():
    """Get the current weight version."""
    return {
        "weight_version": _global_state.tokenizer_manager.server_args.weight_version
    }


@app.get("/get_server_info")
async def get_server_info():
    """Get the server information (deprecated - use /server_info instead)."""
    logger.warning(
        "Endpoint '/get_server_info' is deprecated and will be removed in a future version. "
        "Please use '/server_info' instead."
    )
    return await server_info()


@app.get("/server_info")
async def server_info():
    """Get the server information."""
    # Returns internal states per DP.
    internal_states: List[Dict[Any, Any]] = (
        await _global_state.tokenizer_manager.get_internal_state()
    )

    # This field is not serializable.
    if hasattr(_global_state.tokenizer_manager.server_args, "model_config"):
        del _global_state.tokenizer_manager.server_args.model_config

    return {
        **dataclasses.asdict(_global_state.tokenizer_manager.server_args),
        **_global_state.scheduler_info,
        "internal_states": internal_states,
        "version": __version__,
    }


@app.get("/get_load")
async def get_load():
    return await _global_state.tokenizer_manager.get_load()


# example usage:
# curl -s -X POST http://localhost:30000/set_internal_state -H "Content-Type: application/json" -d '{"server_args": {"pp_max_micro_batch_size": 8}}'
@app.api_route("/set_internal_state", methods=["POST", "PUT"])
async def set_internal_state(obj: SetInternalStateReq, request: Request):
    res = await _global_state.tokenizer_manager.set_internal_state(obj)
    return res


# fastapi implicitly converts json in the request to obj (dataclass)
@app.api_route("/generate", methods=["POST", "PUT"])
async def generate_request(obj: GenerateReqInput, request: Request):
    """Handle a generate request."""
    if obj.stream:

        async def stream_results() -> AsyncIterator[bytes]:
            try:
                async for out in _global_state.tokenizer_manager.generate_request(
                    obj, request
                ):
                    yield b"data: " + orjson.dumps(
                        out, option=orjson.OPT_NON_STR_KEYS
                    ) + b"\n\n"
            except ValueError as e:
                out = {"error": {"message": str(e)}}
                logger.error(f"[http_server] Error: {e}")
                yield b"data: " + orjson.dumps(
                    out, option=orjson.OPT_NON_STR_KEYS
                ) + b"\n\n"
            yield b"data: [DONE]\n\n"

        return StreamingResponse(
            stream_results(),
            media_type="text/event-stream",
            background=_global_state.tokenizer_manager.create_abort_task(obj),
        )
    else:
        try:
            ret = await _global_state.tokenizer_manager.generate_request(
                obj, request
            ).__anext__()
            return ret
        except ValueError as e:
            logger.error(f"[http_server] Error: {e}")
            return _create_error_response(e)


@app.api_route("/generate_from_file", methods=["POST"])
async def generate_from_file_request(file: UploadFile, request: Request):
    """Handle a generate request, this is purely to work with input_embeds."""
    content = await file.read()
    input_embeds = orjson.loads(content.decode("utf-8"))

    obj = GenerateReqInput(
        input_embeds=input_embeds,
        sampling_params={
            "temperature": 0.0,
            "max_new_tokens": 512,
        },
    )

    try:
        ret = await _global_state.tokenizer_manager.generate_request(
            obj, request
        ).__anext__()
        return ret
    except ValueError as e:
        logger.error(f"Error: {e}")
        return _create_error_response(e)


@app.api_route("/encode", methods=["POST", "PUT"])
async def encode_request(obj: EmbeddingReqInput, request: Request):
    """Handle an embedding request."""
    try:
        ret = await _global_state.tokenizer_manager.generate_request(
            obj, request
        ).__anext__()
        return ret
    except ValueError as e:
        return _create_error_response(e)


@app.api_route("/classify", methods=["POST", "PUT"])
async def classify_request(obj: EmbeddingReqInput, request: Request):
    """Handle a reward model request. Now the arguments and return values are the same as embedding models."""
    try:
        ret = await _global_state.tokenizer_manager.generate_request(
            obj, request
        ).__anext__()
        return ret
    except ValueError as e:
        return _create_error_response(e)


@app.api_route("/flush_cache", methods=["GET", "POST"])
async def flush_cache():
    """Flush the radix cache."""
    ret = await _global_state.tokenizer_manager.flush_cache()
    return Response(
        content="Cache flushed.\nPlease check backend logs for more details. "
        "(When there are running or waiting requests, the operation will not be performed.)\n",
        status_code=200 if ret.success else HTTPStatus.BAD_REQUEST,
    )


@app.api_route("/clear_hicache_storage_backend", methods=["GET", "POST"])
async def clear_hicache_storage_backend():
    """Clear the hierarchical cache storage backend."""
    ret = await _global_state.tokenizer_manager.clear_hicache_storage()
    return Response(
        content="Hierarchical cache storage backend cleared.\n",
        status_code=200 if ret.success else HTTPStatus.BAD_REQUEST,
    )


@app.api_route("/start_profile", methods=["GET", "POST"])
async def start_profile_async(obj: Optional[ProfileReqInput] = None):
    """Start profiling."""
    if obj is None:
        obj = ProfileReqInput()

    await _global_state.tokenizer_manager.start_profile(
        output_dir=obj.output_dir,
        start_step=obj.start_step,
        num_steps=obj.num_steps,
        activities=obj.activities,
        with_stack=obj.with_stack,
        record_shapes=obj.record_shapes,
        profile_by_stage=obj.profile_by_stage,
        merge_profiles=obj.merge_profiles,
        profile_prefix=obj.profile_prefix,
        profile_stages=obj.profile_stages,
    )
    return Response(
        content="Start profiling.\n",
        status_code=200,
    )


@app.api_route("/stop_profile", methods=["GET", "POST"])
async def stop_profile_async():
    """Stop profiling."""
    await _global_state.tokenizer_manager.stop_profile()
    return Response(
        content="Stop profiling. This will take some time.\n",
        status_code=200,
    )


@app.api_route("/freeze_gc", methods=["GET", "POST"])
async def freeze_gc_async():
    """
    See engine.freeze_gc for more details.
    """
    await _global_state.tokenizer_manager.freeze_gc()
    return Response(
        content="Garbage collection frozen.\n",
        status_code=200,
    )


@app.api_route("/start_expert_distribution_record", methods=["GET", "POST"])
async def start_expert_distribution_record_async():
    """Start recording the expert distribution. Clear the previous record if any."""
    await _global_state.tokenizer_manager.start_expert_distribution_record()
    return Response(
        content="Start recording the expert distribution.\n",
        status_code=200,
    )


@app.api_route("/stop_expert_distribution_record", methods=["GET", "POST"])
async def stop_expert_distribution_record_async():
    """Stop recording the expert distribution."""
    await _global_state.tokenizer_manager.stop_expert_distribution_record()
    return Response(
        content="Stop recording the expert distribution.\n",
        status_code=200,
    )


@app.api_route("/dump_expert_distribution_record", methods=["GET", "POST"])
async def dump_expert_distribution_record_async():
    """Dump expert distribution record."""
    await _global_state.tokenizer_manager.dump_expert_distribution_record()
    return Response(
        content="Dump expert distribution record.\n",
        status_code=200,
    )


@app.post("/update_weights_from_disk")
async def update_weights_from_disk(obj: UpdateWeightFromDiskReqInput, request: Request):
    """Update the weights from disk inplace without re-launching the server."""
    success, message, num_paused_requests = (
        await _global_state.tokenizer_manager.update_weights_from_disk(obj, request)
    )

    content = {
        "success": success,
        "message": message,
        "num_paused_requests": num_paused_requests,
    }
    if success:
        return ORJSONResponse(
            content,
            status_code=HTTPStatus.OK,
        )
    else:
        return ORJSONResponse(
            content,
            status_code=HTTPStatus.BAD_REQUEST,
        )


@app.post("/init_weights_send_group_for_remote_instance")
async def init_weights_send_group_for_remote_instance(
    obj: InitWeightsSendGroupForRemoteInstanceReqInput, request: Request
):
    success, message = (
        await _global_state.tokenizer_manager.init_weights_send_group_for_remote_instance(
            obj, request
        )
    )
    content = {"success": success, "message": message}
    if success:
        return ORJSONResponse(content, status_code=200)
    else:
        return ORJSONResponse(content, status_code=HTTPStatus.BAD_REQUEST)


@app.post("/send_weights_to_remote_instance")
async def send_weights_to_remote_instance(
    obj: SendWeightsToRemoteInstanceReqInput, request: Request
):
    success, message = (
        await _global_state.tokenizer_manager.send_weights_to_remote_instance(
            obj, request
        )
    )
    content = {"success": success, "message": message}
    if success:
        return ORJSONResponse(content, status_code=200)
    else:
        return ORJSONResponse(content, status_code=HTTPStatus.BAD_REQUEST)


@app.post("/init_weights_update_group")
async def init_weights_update_group(
    obj: InitWeightsUpdateGroupReqInput, request: Request
):
    """Initialize the parameter update group."""
    success, message = await _global_state.tokenizer_manager.init_weights_update_group(
        obj, request
    )
    content = {"success": success, "message": message}
    if success:
        return ORJSONResponse(content, status_code=200)
    else:
        return ORJSONResponse(content, status_code=HTTPStatus.BAD_REQUEST)


@app.post("/destroy_weights_update_group")
async def destroy_weights_update_group(
    obj: DestroyWeightsUpdateGroupReqInput, request: Request
):
    """Destroy the parameter update group."""
    success, message = (
        await _global_state.tokenizer_manager.destroy_weights_update_group(obj, request)
    )
    content = {"success": success, "message": message}
    return ORJSONResponse(
        content, status_code=200 if success else HTTPStatus.BAD_REQUEST
    )


@app.post("/update_weights_from_tensor")
async def update_weights_from_tensor(
    obj: UpdateWeightsFromTensorReqInput, request: Request
):
    """Update the weights from tensor inplace without re-launching the server.
    Notes:
    1. Ensure that the model is on the correct device (e.g., GPU) before calling this endpoint. If the model is moved to the CPU unexpectedly, it may cause performance issues or runtime errors.
    2. HTTP will transmit only the metadata of the tensor, while the tensor itself will be directly copied to the model.
    3. Any binary data in the named tensors should be base64 encoded.
    """

    success, message = await _global_state.tokenizer_manager.update_weights_from_tensor(
        obj, request
    )

    content = {"success": success, "message": message}
    return ORJSONResponse(
        content, status_code=200 if success else HTTPStatus.BAD_REQUEST
    )


@app.post("/update_weights_from_distributed")
async def update_weights_from_distributed(
    obj: UpdateWeightsFromDistributedReqInput, request: Request
):
    """Update model parameter from distributed online."""
    success, message = (
        await _global_state.tokenizer_manager.update_weights_from_distributed(
            obj, request
        )
    )

    content = {"success": success, "message": message}
    if success:
        return ORJSONResponse(content, status_code=200)
    else:
        return ORJSONResponse(content, status_code=HTTPStatus.BAD_REQUEST)


@app.post("/update_weights_from_ipc")
async def update_weights_from_ipc(obj: UpdateWeightsFromIPCReqInput, request: Request):
    """Update the weights from IPC (Inter-Process Communication) for checkpoint-engine integration."""
    success, message = await _global_state.tokenizer_manager.update_weights_from_ipc(
        obj, request
    )

    content = {"success": success, "message": message}
    if success:
        if _global_state.tokenizer_manager.initial_weights_loaded is False:
            _global_state.tokenizer_manager.initial_weights_loaded = True
        return ORJSONResponse(content)
    else:
        return ORJSONResponse(content, status_code=HTTPStatus.BAD_REQUEST)


@app.post("/update_weight_version")
async def update_weight_version(obj: UpdateWeightVersionReqInput, request: Request):
    """Update the weight version. This operation requires no active requests."""
    if obj.abort_all_requests:
        _global_state.tokenizer_manager.abort_request(abort_all=True)

    # Use a simple approach without the complex lock mechanism for now
    # since weight_version update is a simple operation that doesn't affect model weights
    try:
        # Update the weight version in server args (the single source of truth)
        _global_state.tokenizer_manager.server_args.weight_version = obj.new_version

        return ORJSONResponse(
            {
                "success": True,
                "message": f"Weight version updated to {obj.new_version}",
                "new_version": obj.new_version,
            },
            status_code=HTTPStatus.OK,
        )
    except Exception as e:
        return ORJSONResponse(
            {
                "success": False,
                "message": f"Failed to update weight version: {str(e)}",
            },
            status_code=HTTPStatus.BAD_REQUEST,
        )


@app.api_route("/get_weights_by_name", methods=["GET", "POST"])
async def get_weights_by_name(obj: GetWeightsByNameReqInput, request: Request):
    """Get model parameter by name."""
    try:
        ret = await _global_state.tokenizer_manager.get_weights_by_name(obj, request)
        if ret is None:
            return _create_error_response("Get parameter by name failed")
        else:
            return ORJSONResponse(ret, status_code=200)
    except Exception as e:
        return _create_error_response(e)


@app.api_route("/release_memory_occupation", methods=["GET", "POST"])
async def release_memory_occupation(
    obj: ReleaseMemoryOccupationReqInput, request: Request
):
    """Release GPU memory occupation temporarily."""
    try:
        await _global_state.tokenizer_manager.release_memory_occupation(obj, request)
    except Exception as e:
        return _create_error_response(e)


@app.api_route("/resume_memory_occupation", methods=["GET", "POST"])
async def resume_memory_occupation(
    obj: ResumeMemoryOccupationReqInput, request: Request
):
    """Resume GPU memory occupation."""
    try:
        await _global_state.tokenizer_manager.resume_memory_occupation(obj, request)
    except Exception as e:
        return _create_error_response(e)


@app.post("/weights_checker")
async def check_weights(obj: CheckWeightsReqInput, request: Request):
    success, message = await _global_state.tokenizer_manager.check_weights(obj, request)
    return ORJSONResponse(
        {"success": success, "message": message},
        status_code=200 if success else HTTPStatus.BAD_REQUEST,
    )


@app.api_route("/slow_down", methods=["GET", "POST"])
async def slow_down(obj: SlowDownReqInput, request: Request):
    """Slow down the system deliberately. Only for testing. Example scenario:
    when we want to test performance of D in large-scale PD disaggregation and have no enough nodes for P,
    we can use this to slow down D to let it have enough running sequences, and then disable slowdown
    to let it run in full batch size.
    """
    try:
        await _global_state.tokenizer_manager.slow_down(obj, request)
    except Exception as e:
        return _create_error_response(e)


@app.api_route("/load_lora_adapter", methods=["POST"])
async def load_lora_adapter(obj: LoadLoRAAdapterReqInput, request: Request):
    """Load a new LoRA adapter without re-launching the server."""
    result = await _global_state.tokenizer_manager.load_lora_adapter(obj, request)

    if result.success:
        return ORJSONResponse(
            result,
            status_code=HTTPStatus.OK,
        )
    else:
        return ORJSONResponse(
            result,
            status_code=HTTPStatus.BAD_REQUEST,
        )


@app.api_route("/unload_lora_adapter", methods=["POST"])
async def unload_lora_adapter(obj: UnloadLoRAAdapterReqInput, request: Request):
    """Load a new LoRA adapter without re-launching the server."""
    result = await _global_state.tokenizer_manager.unload_lora_adapter(obj, request)

    if result.success:
        return ORJSONResponse(
            result,
            status_code=HTTPStatus.OK,
        )
    else:
        return ORJSONResponse(
            result,
            status_code=HTTPStatus.BAD_REQUEST,
        )


@app.api_route("/open_session", methods=["GET", "POST"])
async def open_session(obj: OpenSessionReqInput, request: Request):
    """Open a session, and return its unique session id."""
    try:
        session_id = await _global_state.tokenizer_manager.open_session(obj, request)
        if session_id is None:
            raise Exception(
                "Failed to open the session. Check if a session with the same id is still open."
            )
        return session_id
    except Exception as e:
        return _create_error_response(e)


@app.api_route("/close_session", methods=["GET", "POST"])
async def close_session(obj: CloseSessionReqInput, request: Request):
    """Close the session."""
    try:
        await _global_state.tokenizer_manager.close_session(obj, request)
        return Response(status_code=200)
    except Exception as e:
        return _create_error_response(e)


@app.api_route("/configure_logging", methods=["GET", "POST"])
async def configure_logging(obj: ConfigureLoggingReq, request: Request):
    """Configure the request logging options."""
    _global_state.tokenizer_manager.configure_logging(obj)
    return Response(status_code=200)


@app.post("/abort_request")
async def abort_request(obj: AbortReq, request: Request):
    """Abort a request."""
    try:
        _global_state.tokenizer_manager.abort_request(
            rid=obj.rid, abort_all=obj.abort_all
        )
        return Response(status_code=200)
    except Exception as e:
        return _create_error_response(e)


@app.post("/parse_function_call")
async def parse_function_call_request(obj: ParseFunctionCallReq, request: Request):
    """
    A native API endpoint to parse function calls from a text.
    """
    # 1) Initialize the parser based on the request body
    parser = FunctionCallParser(tools=obj.tools, tool_call_parser=obj.tool_call_parser)

    # 2) Call the non-stream parsing method (non-stream)
    normal_text, calls = parser.parse_non_stream(obj.text)

    # 3) Organize the response content
    response_data = {
        "normal_text": normal_text,
        "calls": [
            call.model_dump() for call in calls
        ],  # Convert pydantic objects to dictionaries
    }

    return ORJSONResponse(content=response_data, status_code=200)


@app.post("/separate_reasoning")
async def separate_reasoning_request(obj: SeparateReasoningReqInput, request: Request):
    """
    A native API endpoint to separate reasoning from a text.
    """
    # 1) Initialize the parser based on the request body
    parser = ReasoningParser(model_type=obj.reasoning_parser)

    # 2) Call the non-stream parsing method (non-stream)
    reasoning_text, normal_text = parser.parse_non_stream(obj.text)

    # 3) Organize the response content
    response_data = {
        "reasoning_text": reasoning_text,
        "text": normal_text,
    }

    return ORJSONResponse(content=response_data, status_code=200)


@app.post("/pause_generation")
async def pause_generation(obj: PauseGenerationReqInput, request: Request):
    """Pause generation."""
    await _global_state.tokenizer_manager.pause_generation(obj)
    return ORJSONResponse(
        content={"message": "Generation paused successfully.", "status": "ok"},
        status_code=200,
    )


@app.post("/continue_generation")
async def continue_generation(obj: ContinueGenerationReqInput, request: Request):
    """Continue generation."""
    await _global_state.tokenizer_manager.continue_generation(obj)
    return ORJSONResponse(
        content={"message": "Generation continued successfully.", "status": "ok"},
        status_code=200,
    )


##### OpenAI-compatible API endpoints #####


@app.post("/v1/completions", dependencies=[Depends(validate_json_request)])
async def openai_v1_completions(request: CompletionRequest, raw_request: Request):
    """OpenAI-compatible text completion endpoint."""
    return await raw_request.app.state.openai_serving_completion.handle_request(
        request, raw_request
    )


@app.post("/v1/chat/completions", dependencies=[Depends(validate_json_request)])
async def openai_v1_chat_completions(
    request: ChatCompletionRequest, raw_request: Request
):
    """OpenAI-compatible chat completion endpoint."""
    return await raw_request.app.state.openai_serving_chat.handle_request(
        request, raw_request
    )


@app.post(
    "/v1/embeddings",
    response_class=ORJSONResponse,
    dependencies=[Depends(validate_json_request)],
)
async def openai_v1_embeddings(request: EmbeddingRequest, raw_request: Request):
    """OpenAI-compatible embeddings endpoint."""
    return await raw_request.app.state.openai_serving_embedding.handle_request(
        request, raw_request
    )


@app.post(
    "/v1/classify",
    response_class=ORJSONResponse,
    dependencies=[Depends(validate_json_request)],
)
async def openai_v1_classify(request: ClassifyRequest, raw_request: Request):
    """OpenAI-compatible classification endpoint."""
    return await raw_request.app.state.openai_serving_classify.handle_request(
        request, raw_request
    )


@app.post(
    "/v1/tokenize",
    response_class=ORJSONResponse,
    dependencies=[Depends(validate_json_request)],
)
@app.post(
    "/tokenize",
    response_class=ORJSONResponse,
    dependencies=[Depends(validate_json_request)],
    include_in_schema=False,
)
async def openai_v1_tokenize(request: TokenizeRequest, raw_request: Request):
    """OpenAI-compatible tokenization endpoint."""
    return await raw_request.app.state.openai_serving_tokenize.handle_request(
        request, raw_request
    )


@app.post(
    "/v1/detokenize",
    response_class=ORJSONResponse,
    dependencies=[Depends(validate_json_request)],
)
@app.post(
    "/detokenize",
    response_class=ORJSONResponse,
    dependencies=[Depends(validate_json_request)],
    include_in_schema=False,
)
async def openai_v1_detokenize(request: DetokenizeRequest, raw_request: Request):
    """OpenAI-compatible detokenization endpoint."""
    return await raw_request.app.state.openai_serving_detokenize.handle_request(
        request, raw_request
    )


@app.get("/v1/models", response_class=ORJSONResponse)
async def available_models():
    """Show available models. OpenAI-compatible endpoint."""
    served_model_names = [_global_state.tokenizer_manager.served_model_name]
    model_cards = []

    # Add base model
    for served_model_name in served_model_names:
        model_cards.append(
            ModelCard(
                id=served_model_name,
                root=served_model_name,
                max_model_len=_global_state.tokenizer_manager.model_config.context_len,
            )
        )

    # Add loaded LoRA adapters
    if _global_state.tokenizer_manager.server_args.enable_lora:
        lora_registry = _global_state.tokenizer_manager.lora_registry
        for _, lora_ref in lora_registry.get_all_adapters().items():
            model_cards.append(
                ModelCard(
                    id=lora_ref.lora_name,
                    root=lora_ref.lora_path,
                    parent=served_model_names[0],
                    max_model_len=None,
                )
            )

    return ModelList(data=model_cards)


@app.get("/v1/models/{model:path}", response_class=ORJSONResponse)
async def retrieve_model(model: str):
    """Retrieves a model instance, providing basic information about the model."""
    served_model_names = [_global_state.tokenizer_manager.served_model_name]

    if model not in served_model_names:
        return ORJSONResponse(
            status_code=404,
            content={
                "error": {
                    "message": f"The model '{model}' does not exist",
                    "type": "invalid_request_error",
                    "param": "model",
                    "code": "model_not_found",
                }
            },
        )

    return ModelCard(
        id=model,
        root=model,
        max_model_len=_global_state.tokenizer_manager.model_config.context_len,
    )


@app.post("/v1/score", dependencies=[Depends(validate_json_request)])
async def v1_score_request(request: ScoringRequest, raw_request: Request):
    """Endpoint for the decoder-only scoring API. See Engine.score() for detailed documentation."""
    return await raw_request.app.state.openai_serving_score.handle_request(
        request, raw_request
    )


@app.post("/v1/responses", dependencies=[Depends(validate_json_request)])
async def v1_responses_request(request: dict, raw_request: Request):
    """Endpoint for the responses API with reasoning support."""

    request_obj = ResponsesRequest(**request)
    result = await raw_request.app.state.openai_serving_responses.create_responses(
        request_obj, raw_request
    )

    # Handle streaming responses
    if isinstance(result, AsyncGenerator):
        return StreamingResponse(
            result,
            media_type="text/event-stream",
            headers={"Cache-Control": "no-cache", "Connection": "keep-alive"},
        )

    return result


@app.get("/v1/responses/{response_id}")
async def v1_retrieve_responses(response_id: str, raw_request: Request):
    """Retrieve a response by ID."""
    return await raw_request.app.state.openai_serving_responses.retrieve_responses(
        response_id
    )


@app.post("/v1/responses/{response_id}/cancel")
async def v1_cancel_responses(response_id: str, raw_request: Request):
    """Cancel a background response."""
    return await raw_request.app.state.openai_serving_responses.cancel_responses(
        response_id
    )


@app.api_route(
    "/v1/rerank", methods=["POST", "PUT"], dependencies=[Depends(validate_json_request)]
)
async def v1_rerank_request(request: V1RerankReqInput, raw_request: Request):
    """Endpoint for reranking documents based on query relevance."""
    return await raw_request.app.state.openai_serving_rerank.handle_request(
        request, raw_request
    )


## SageMaker API
@app.get("/ping")
async def sagemaker_health() -> Response:
    """Check the health of the http server."""
    return Response(status_code=200)


@app.post("/invocations")
async def sagemaker_chat_completions(
    request: ChatCompletionRequest, raw_request: Request
):
    """OpenAI-compatible chat completion endpoint."""
    return await raw_request.app.state.openai_serving_chat.handle_request(
        request, raw_request
    )


## Vertex AI API
@app.post(os.environ.get("AIP_PREDICT_ROUTE", "/vertex_generate"))
async def vertex_generate(vertex_req: VertexGenerateReqInput, raw_request: Request):
    if not vertex_req.instances:
        return []
    inputs = {}
    for input_key in ("text", "input_ids", "input_embeds"):
        if vertex_req.instances[0].get(input_key):
            inputs[input_key] = [
                instance.get(input_key) for instance in vertex_req.instances
            ]
            break
    image_data = [
        instance.get("image_data")
        for instance in vertex_req.instances
        if instance.get("image_data") is not None
    ] or None
    req = GenerateReqInput(
        **inputs,
        image_data=image_data,
        **(vertex_req.parameters or {}),
    )
    ret = await generate_request(req, raw_request)
    if isinstance(ret, Response):
        return ret
    return ORJSONResponse({"predictions": ret})


def _create_error_response(e):
    return ORJSONResponse(
        {"error": {"message": str(e)}}, status_code=HTTPStatus.BAD_REQUEST
    )


def launch_server(
    server_args: ServerArgs,
    pipe_finish_writer: Optional[multiprocessing.connection.Connection] = None,
    launch_callback: Optional[Callable[[], None]] = None,
):
    """
    Launch SRT (SGLang Runtime) Server.

    The SRT server consists of an HTTP server and an SRT engine.

    - HTTP server: A FastAPI server that routes requests to the engine.
    - The engine consists of three components:
        1. TokenizerManager: Tokenizes the requests and sends them to the scheduler.
        2. Scheduler (subprocess): Receives requests from the Tokenizer Manager, schedules batches, forwards them, and sends the output tokens to the Detokenizer Manager.
        3. DetokenizerManager (subprocess): Detokenizes the output tokens and sends the result back to the Tokenizer Manager.

    Note:
    1. The HTTP server, Engine, and TokenizerManager all run in the main process.
    2. Inter-process communication is done through IPC (each process uses a different port) via the ZMQ library.
    """
    tokenizer_manager, template_manager, scheduler_info, port_args = (
        _launch_subprocesses(server_args=server_args)
    )

    set_global_state(
        _GlobalState(
            tokenizer_manager=tokenizer_manager,
            template_manager=template_manager,
            scheduler_info=scheduler_info,
        )
    )

    # Pass additional arguments to the lifespan function.
    # They will be used for additional initialization setups.
    if server_args.tokenizer_worker_num == 1:
        # If it is single tokenizer mode, we can pass the arguments by attributes of the app object.
        app.is_single_tokenizer_mode = True
        app.server_args = server_args
        app.warmup_thread_args = (
            server_args,
            pipe_finish_writer,
            launch_callback,
        )

        # Add api key authorization
        # This is only supported in single tokenizer mode.
        if server_args.api_key:
            add_api_key_middleware(app, server_args.api_key)
    else:
        # If it is multi-tokenizer mode, we need to write the arguments to shared memory
        # for other worker processes to read.
        app.is_single_tokenizer_mode = False
        multi_tokenizer_args_shm = write_data_for_multi_tokenizer(
            port_args, server_args, scheduler_info
        )

    try:
        # Update logging configs
        set_uvicorn_logging_configs()

        # Listen for HTTP requests
        if server_args.tokenizer_worker_num == 1:
            uvicorn.run(
                app,
                host=server_args.host,
                port=server_args.port,
                root_path=server_args.fastapi_root_path,
                log_level=server_args.log_level_http or server_args.log_level,
                timeout_keep_alive=5,
                loop="uvloop",
            )
        else:
            from uvicorn.config import LOGGING_CONFIG

            LOGGING_CONFIG["loggers"]["sglang.srt.entrypoints.http_server"] = {
                "handlers": ["default"],
                "level": "INFO",
                "propagate": False,
            }
            monkey_patch_uvicorn_multiprocessing()

            uvicorn.run(
                "sglang.srt.entrypoints.http_server:app",
                host=server_args.host,
                port=server_args.port,
                root_path=server_args.fastapi_root_path,
                log_level=server_args.log_level_http or server_args.log_level,
                timeout_keep_alive=5,
                loop="uvloop",
                workers=server_args.tokenizer_worker_num,
            )
    finally:
        if server_args.tokenizer_worker_num > 1:
            multi_tokenizer_args_shm.unlink()
            _global_state.tokenizer_manager.socket_mapping.clear_all_sockets()


# Minimal 32x32 black PNG (base64, GLM4v requires at least 32x32 sized image)
MINIMUM_PNG_PICTURE_BASE64 = "iVBORw0KGgoAAAANSUhEUgAAACAAAAAgCAYAAABzenr0AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAbUlEQVRYhe3VsQ2AMAxE0Y/lIgNQULD/OqyCMgCihCKSG4yRuKuiNH6JLsoEbMACOGBcua9HOR7Y6w6swBwMy0qLTpkeI77qdEBpBFAHBBDAGH8WrwJKI4AAegUCfAKgEgpQDvh3CR3oQCuav58qlAw73kKCSgAAAABJRU5ErkJggg=="


def _execute_server_warmup(
    server_args: ServerArgs,
    pipe_finish_writer: Optional[multiprocessing.connection.Connection],
):
    headers = {}
    url = server_args.url()
    if server_args.api_key:
        headers["Authorization"] = f"Bearer {server_args.api_key}"

    # Wait until the server is launched
    success = False
    for _ in range(120):
        time.sleep(1)
        try:
            res = requests.get(url + "/get_model_info", timeout=5, headers=headers)
            assert res.status_code == 200, f"{res=}, {res.text=}"
            success = True
            break
        except (AssertionError, requests.exceptions.RequestException):
            last_traceback = get_exception_traceback()
            pass

    if not success:
        if pipe_finish_writer is not None:
            pipe_finish_writer.send(last_traceback)
        logger.error(f"Initialization failed. warmup error: {last_traceback}")
        kill_process_tree(os.getpid())
        return success

    model_info = res.json()

    is_vlm = bool(model_info.get("has_image_understanding", False))

    # Send a warmup request
    if model_info["is_generation"]:
        if is_vlm and not server_args.skip_tokenizer_init:
            request_name = "/v1/chat/completions"
        else:
            request_name = "/generate"
    else:
        request_name = "/encode"
    max_new_tokens = 8 if model_info["is_generation"] else 1
    json_data = {
        "sampling_params": {
            "temperature": 0,
            "max_new_tokens": max_new_tokens,
        },
    }
    if server_args.skip_tokenizer_init:
        json_data["input_ids"] = [[10, 11, 12] for _ in range(server_args.dp_size)]
        # TODO Workaround the bug that embedding errors for list of size 1
        if server_args.dp_size == 1:
            json_data["input_ids"] = json_data["input_ids"][0]
    elif is_vlm and server_args.disaggregation_mode == "null":
        # TODO: ChatCompletionRequest does not have bootstrap info required by disaggregation mode, disable image-warmup for now
        json_data = {
            "model": _global_state.tokenizer_manager.served_model_name,
            "messages": [
                {
                    "role": "user",
                    "content": [
                        {
                            "type": "image_url",
                            "image_url": {
                                "url": f"data:image/png;base64,{MINIMUM_PNG_PICTURE_BASE64}"
                            },
                        },
                        {
                            "type": "text",
                            "text": "Describe the image.",
                        },
                    ],
                }
            ],
            "max_tokens": max_new_tokens,
            "stream": False,
            "temperature": 0.0,
        }
    else:
        json_data["text"] = ["The capital city of France is"] * server_args.dp_size
        # TODO Workaround the bug that embedding errors for list of size 1
        if server_args.dp_size == 1:
            json_data["text"] = json_data["text"][0]

    # Debug dumping
    if server_args.debug_tensor_dump_input_file:
        json_data.pop("text", None)
        json_data["input_ids"] = np.load(
            server_args.debug_tensor_dump_input_file
        ).tolist()
        json_data["sampling_params"]["max_new_tokens"] = 0

    try:
        warmup_timeout = envs.SGLANG_WARMUP_TIMEOUT.get()
        if server_args.disaggregation_mode == "null":
            res = requests.post(
                url + request_name,
                json=json_data,
                headers=headers,
                timeout=warmup_timeout if warmup_timeout > 0 else 600,
            )
            assert res.status_code == 200, f"{res.text}"
            _global_state.tokenizer_manager.server_status = ServerStatus.Up

        else:
            logger.info(f"Start of pd disaggregation warmup ...")
            json_data = {
                "sampling_params": {
                    "temperature": 0.0,
                    "max_new_tokens": 8,
                    "ignore_eos": True,
                },
                "bootstrap_host": [FAKE_BOOTSTRAP_HOST] * server_args.dp_size,
                # This is a hack to ensure fake transfer is enabled during prefill warmup
                # ensure each dp rank has a unique bootstrap_room during prefill warmup
                "bootstrap_room": [
                    i * (2**63 // server_args.dp_size) + (i % server_args.tp_size)
                    for i in range(server_args.dp_size)
                ],
                "input_ids": [[0, 1, 2, 3]] * server_args.dp_size,
            }
            res = requests.post(
                url + request_name,
                json=json_data,
                headers=headers,
                timeout=(
                    warmup_timeout if warmup_timeout > 0 else 1800
                ),  # because of deep gemm precache is very long if not precache.
            )
            if res.status_code == 200:
                logger.info(
                    f"End of prefill disaggregation mode warmup with status {res.status_code}, resp: {res.json()}"
                )
                _global_state.tokenizer_manager.server_status = ServerStatus.Up
            else:
                logger.info(
                    "Prefill disaggregation mode warm Up Failed, status code: {}".format(
                        res.status_code
                    )
                )
                _global_state.tokenizer_manager.server_status = ServerStatus.UnHealthy

    except Exception:
        last_traceback = get_exception_traceback()
        if pipe_finish_writer is not None:
            pipe_finish_writer.send(last_traceback)
        logger.error(f"Initialization failed. warmup error: {last_traceback}")
        kill_process_tree(os.getpid())
        return False

    # Debug print
    # logger.info(f"warmup request returns: {res.json()=}")
    return success


def _wait_and_warmup(
    server_args: ServerArgs,
    pipe_finish_writer: Optional[multiprocessing.connection.Connection],
    launch_callback: Optional[Callable[[], None]] = None,
):
    if server_args.checkpoint_engine_wait_weights_before_ready:
        _wait_weights_ready()
    if not server_args.skip_server_warmup:
        if not _execute_server_warmup(
            server_args,
            pipe_finish_writer,
        ):
            return
    else:
        _global_state.tokenizer_manager.server_status = ServerStatus.Up

    logger.info("The server is fired up and ready to roll!")

    if pipe_finish_writer is not None:
        pipe_finish_writer.send("ready")

    if server_args.delete_ckpt_after_loading:
        delete_directory(server_args.model_path)

    if server_args.debug_tensor_dump_input_file:
        kill_process_tree(os.getpid())

    if launch_callback is not None:
        launch_callback()


def _wait_weights_ready():
    """Wait for weights to be ready within the specified timeout."""
    timeout = WAIT_WEIGHTS_READY_TIMEOUT
    start_time = time.time()

    for _ in range(timeout):
        if _global_state.tokenizer_manager.initial_weights_loaded:
            logger.info(
                f"Weights are ready after {time.time() - start_time:.2f} seconds"
            )
            return
        time.sleep(1)

    # Timeout reached without weights being ready
    logger.error(
        f"Weights are not ready after waiting {timeout} seconds. "
        f"Consider increasing SGLANG_WAIT_WEIGHTS_READY_TIMEOUT environment variable. "
        f"Current status: initial_weights_loaded={_global_state.tokenizer_manager.initial_weights_loaded}"
    )
